Accelerating Mixture-of-Experts Training with Adaptive Expert Replication

• URL: http://arxiv.org/abs/2504.19925v1
• Date: Mon, 28 Apr 2025 15:58:55 GMT
• Title: Accelerating Mixture-of-Experts Training with Adaptive Expert Replication
• Authors: Athinagoras Skiadopoulos, Mark Zhao, Swapnil Gandhi, Thomas Norrie, Shrijeet Mukherjee, Christos Kozyrakis,
• Abstract summary: We introduce SwiftMoE, an adaptive MoE training system.<n>SwiftMoE right-sizes the GPU resources allocated to each expert, on a per-iteration basis, with minimal overheads.<n>Compared to state-of-the-art MoE training systems, DeepSpeed and FlexMoE, SwiftMoE is able to achieve a 30.5% and 25.9% faster time-to-convergence.
• Score: 1.8764600940655036
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Mixture-of-Experts (MoE) models have become a widely adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training, each input token is dynamically routed to a subset of experts -- sparsely-activated feed-forward networks -- within each transformer layer. The distribution of tokens assigned to each expert varies widely and rapidly over the course of training. To handle the wide load imbalance across experts, current systems are forced to either drop tokens assigned to popular experts, degrading convergence, or frequently rebalance resources allocated to each expert based on popularity, incurring high state migration overheads. To break this performance-accuracy tradeoff, we introduce SwiftMoE, an adaptive MoE training system. The key insight of SwiftMoE is to decouple the placement of expert parameters from their large optimizer state. SwiftMoE statically partitions the optimizer of each expert across all training nodes. Meanwhile, SwiftMoE dynamically adjusts the placement of expert parameters by repurposing existing weight updates, avoiding migration overheads. In doing so, SwiftMoE right-sizes the GPU resources allocated to each expert, on a per-iteration basis, with minimal overheads. Compared to state-of-the-art MoE training systems, DeepSpeed and FlexMoE, SwiftMoE is able to achieve a 30.5% and 25.9% faster time-to-convergence, respectively.

Related papers

• DynaMoE: Dynamic Token-Level Expert Activation with Layer-Wise Adaptive Capacity for Mixture-of-Experts Neural Networks [0.0]
  Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling neural networks while maintaining computational efficiency.<n>This paper introduces DynaMoE, a novel MoE framework that relaxes both constraints through dynamic token-level expert activation and layer-wise adaptive capacity allocation.
  arXiv  Detail & Related papers  (2026-03-02T10:25:56Z)
• A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs [64.8510381475827]
  Sparse Mixture-of-Experts (SMoE) architectures are increasingly used to scale large language models efficiently.<n>SMoE models often suffer from severe load imbalance across experts, where a small subset of experts receives most tokens while others are underutilized.<n>We present a systematic analysis of expert routing during inference and identify three findings: (i) load imbalance persists and worsens with larger batch sizes, (ii) selection frequency does not reliably reflect expert importance, and (iii) overall expert workload and importance can be estimated using a small calibration set.
  arXiv  Detail & Related papers  (2026-02-23T15:11:16Z)
• LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training [27.022187489292467]
  In this paper, we introduce LAER-MoE, an efficient MoE training framework.<n>The core of LAER-MoE is a novel parallel paradigm, Fully Sharded Expert Parallel (FSEP), which fully partitions each expert parameter by the number of devices.<n>We perform experiments on an A100 cluster, and the results indicate that our system achieves up to 1.69x acceleration compared to the current state-of-the-art training systems.
  arXiv  Detail & Related papers  (2026-02-12T08:08:03Z)
• DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems [2.472349172396126]
  Existing batch size selection approaches rely on static allocation or simplistics that fail to adapt to heterogeneous, dynamic computing environments.<n>We present DYNAmix, a reinforcement learning framework that formulates batch size optimization as a sequen- tial decision-making problem using Proximal Policy Optimiza- tion (PPO)<n>Our approach employs a multi-dimensional state representation encompassing network-level metrics, system-level resource utilization, and training statistical efficiency indicators to enable informed decision-making across diverse computational resources.
  arXiv  Detail & Related papers  (2025-10-09T17:48:24Z)
• Training Matryoshka Mixture-of-Experts for Elastic Inference-Time Expert Utilization [60.309915093470416]
  Matryoshka MoE (M-MoE) is a training framework that instills a coarse-to-fine structure directly into the expert ensemble.<n>Our work paves the way for more practical and adaptable deployments of large-scale MoE models.
  arXiv  Detail & Related papers  (2025-09-30T16:56:44Z)
• Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach [52.79991638077892]
  This article highlights a critical, yet underexplored concept: the absence of robust quantitative strategies for dynamic client-expert alignment.<n>We propose a conceptual system design for intelligent client-expert alignment that incorporates dynamic fitness scoring, global expert load monitoring, and client capacity profiling.
  arXiv  Detail & Related papers  (2025-07-08T05:30:37Z)
• Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models [58.54288496296157]
  Chain-of-Experts (CoE) is a new Mixture-of-Experts (MoE) architecture that introduces sequential expert communication within each layer.<n>To support dynamic expert selection across iterations, CoE employs a dedicated router at each step within a layer.
  arXiv  Detail & Related papers  (2025-06-23T02:15:43Z)
• Dense Backpropagation Improves Training for Sparse Mixture-of-Experts [41.08173926456885]
  We present a lightweight approximation method that gives the MoE router a dense gradient update while continuing to sparsely activate its parameters.<n>Our Default MoE outperforms standard TopK routing in a variety of settings without requiring significant computational overhead.
  arXiv  Detail & Related papers  (2025-04-16T19:55:36Z)
• ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration [61.579842548990754]
  Mixture-of-Experts (MoE) Transformer, the backbone of multiple phenomenal language models, leverages sparsity by activating only a fraction of model parameters for each input token.<n>We introduce ResMoE, an innovative MoE approximation framework that utilizes Wasserstein barycenter to extract a common expert (barycenter expert) and approximate the residuals between this barycenter expert and the original ones.
  arXiv  Detail & Related papers  (2025-03-10T03:15:54Z)
• FlexDeMo: Decoupled Momentum Optimization for Hybrid Sharded Data Parallel Training [5.191183730031093]
  Training large neural network models requires extensive computational resources, often distributed across several nodes and accelerators. Recent findings suggest that it may be sufficient to only exchange the fast moving components of the gradients, while accumulating momentum locally (Decoupled Momentum) Here, we propose employing a hybrid sharded data parallel training strategy, FlexDeMo, whereby nodes fully shard model parameters locally between different accelerators.
  arXiv  Detail & Related papers  (2025-02-10T17:55:59Z)
• Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts [0.62914438169038]
  We introduce MixER: Mixture of Expert Reconstructors, a novel sparse top-1 MoE layer employing a custom gating update algorithm based on $K$-means and least squares.<n>Experiments validate MixER's capabilities, demonstrating efficient training and scalability to systems of up to ten ordinary parametric differential equations.<n>Our layer underperforms state-of-the-art meta-learners in high-data regimes, particularly when each expert is constrained to process only a fraction of a dataset composed of highly related data points.
  arXiv  Detail & Related papers  (2025-02-07T21:16:43Z)
• HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference [54.40808356999408]
  We present HOBBIT, a mixed precision expert offloading system to enable flexible and efficient MoE inference. Our key insight is that dynamically replacing less critical cache-miss experts with low precision versions can substantially reduce expert-loading latency. HOBBIT achieves up to a 9.93x speedup in decoding compared to state-of-the-art MoE offloading systems.
  arXiv  Detail & Related papers  (2024-11-03T04:25:46Z)
• Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design [59.00758127310582]
  We propose a novel framework Read-ME that transforms pre-trained dense LLMs into smaller MoE models. Our approach employs activation sparsity to extract experts. Read-ME outperforms other popular open-source dense models of similar scales.
  arXiv  Detail & Related papers  (2024-10-24T19:48:51Z)
• MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts [63.67734699877724]
  MoE++ is a general and heterogeneous MoE framework that integrates both Feed-Forward Network(FFN) and zero-computation experts. MoE++ achieves better performance while delivering 1.1-2.1x expert forward throughput compared to a vanilla MoE model of the same size.
  arXiv  Detail & Related papers  (2024-10-09T18:01:27Z)
• AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference [13.263938935671646]
  AdapMoE is an algorithm-system co-design framework for efficient MoE inference. AdapMoE features adaptive expert gating and management to reduce the on-demand loading overheads. We show AdapMoE consistently outperforms existing techniques, reducing the average number of activated experts by 25% and achieving a 1.35x speedup without degradation accuracy.
  arXiv  Detail & Related papers  (2024-08-19T03:27:15Z)
• Toward Inference-optimal Mixture-of-Expert Large Language Models [55.96674056805708]
  We study the scaling law of MoE-based large language models (LLMs) We find that MoEs with a few (4/8) experts are the most serving efficient solution under the same performance, but costs 2.5-3.5x more in training. We propose to amend the scaling law of MoE by introducing inference efficiency as another metric besides the validation loss.
  arXiv  Detail & Related papers  (2024-04-03T16:33:42Z)
• Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM [81.18305296110853]
  We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains. Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion. BTX generalizes two special cases, the Branch-Train-Merge method, which does not have the MoE finetuning stage to learn routing, and sparse upcycling, which omits the stage of training experts asynchronously.
  arXiv  Detail & Related papers  (2024-03-12T16:54:58Z)
• LocMoE: A Low-Overhead MoE for Large Language Model Training [13.153904674287546]
  We propose a novel routing strategy that combines load balance and locality by converting partial inter-node communication to that of intra-node. The proposed LocMoE reduces training time per epoch by 12.68% to 22.24% compared to classical routers.
  arXiv  Detail & Related papers  (2024-01-25T03:36:39Z)
• Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference [3.217776693788795]
  We propose a lightweight optimization technique called ExFlow to largely accelerate the inference of pre-trained MoE models. By exploiting the inter-layer expert affinity, our solution can be directly applied to pre-trained MoE models without any fine-tuning or accuracy degradation. Our solution beats the cutting-edge MoE implementations with experts from 8 to 64, with up to 2.2x improvement in inference throughput.
  arXiv  Detail & Related papers  (2024-01-16T14:16:47Z)
• Sparse Backpropagation for MoE Training [118.31785160874024]
  We introduce SparseMixer, a scalable gradient estimator that bridges the gap between backpropagation and sparse expert routing. Grounded in a numerical ODE framework, SparseMixer harnesses the mid-point method, a second-order ODE solver, to deliver precise gradient approximations. Applying SparseMixer to Switch Transformer on both pre-training and machine translation tasks, SparseMixer showcases considerable performance gain.
  arXiv  Detail & Related papers  (2023-10-01T22:43:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.